machine learning
2 PhD Positions in Atomistic Simulations and In-situ SEM Experiments at the University of Vermont (USA)
Applications are invited for two doctoral research positions in the Department of Mechanical Engineering at the University of Vermont to begin immediately.
PhD position on machine learning enhanced multi-scale modelling of textile composites at the University of Gothenburg
We have an open PhD position on machine learning enhanced multi-scale modelling of textile composites. The following link provides more information about the project, and the details of the application process. Please keep in mind that only applications sent through the online application system will be evaluated.
Description of the PhD project, and how to apply
Journal Club for February 2020: Machine Learning in Mechanics: simple resources, examples & opportunities
Machine learning (ML) in Mechanics is a fascinating and timely topic. This article follows from a kind invitation to provide some thoughts about the use of ML algorithms to solve mechanics problems by overviewing my past and current research efforts along with students and collaborators in this field. A brief introduction on ML is initially provided for the colleagues not familiar with the topic, followed by a section about the usefulness of ML in Mechanics, and finally I will reflect on the challenges and opportunities in this field.
[Deadlines updated] ICTAM2020 & WCCM2020
Dear colleagues,
Deadline to submit your abstract to ICTAM2020 and WCCM2020 is fast approacing (January 20 & 15, respectively). If you are working with machine learning, uncertainty quantification, optimization or a related topic, consider the following symposia:
Postdoctoral position in the Hopkins Extreme Materials Institute on the Biomechanics of Traumatic Brain Injury in Humans
Biomechanics of Traumatic Brain Injury
Prediction of forming limit diagrams using machine learning
Measuring forming limit diagrams (FLDs) is a time consuming and expensive process. Machine learning (ML) methods are a promising route to predict FLD of aluminium alloys. In the present work, we developed a machine learning (ML) based tool to establish the relationships between alloy composition / thermomechanical processing route to the material's FLD.
Session on "Data driven materials science" at the DPG Spring Meeting (Dresden, Germany)
Dear colleagues,
we would like to make you aware of the topical session
"Data driven materials science"
which is part of the MM program during the DPG Spring Meeting 2020. The latter takes place March 15-20, 2020, in Dresden.
If you are performing experiments or simulations in this emerging field, you are most welcome to contribute your abstract. You can find the session at the bottom of the list "Themenbereiche" on the abstract submission webpage
Senior Modeling Scientist @ Novelis Global Research and Technology Center
Requisition Title:Senior Modeling Scientist Job Number::190106PI
Schedule
:Full-time
Primary Location
:USA-GA-Kennesaw (Global R&T)
Organization
:Global R&T
Job Type
:Standard
Job
:Research & Development
A nonlinear data-driven reduced order model for computational homogenization with physics/pattern-guided sampling
Developing an accurate nonlinear reduced order model from simulation data has been an outstanding research topic for many years. For many physical systems, data collection is very expensive and the optimal data distribution is not known in advance. Thus, maximizing the information gain remains a grand challenge. In a recent paper, Bhattacharjee and Matous (2016) proposed a manifold-based nonlinear reduced order model for multiscale problems in mechanics of materials. Expanding this work here, we develop a novel sampling strategy based on the physics/pattern-guided data distribution.